We now present TransBoost, a procedure that takes a pretrained neural model along with its (labeled) training set as well as an (unlabeled) test set. The procedure then fine-tunes the model to improve its performance for this specific test set.
The TransBoost optimization objective, \(\mathcal {L}\) , is driven by Equation (REF ) and approximated by Algorithm , which includes a novel transductive loss component in addition to the standard cross-entropy loss. Our transductive component, \(\mathcal {L}_\text{TransBoost}\) , is introduced in Equation (REF ) as a regularization term and is defined in Equation (REF ).
\(\mathcal {L}_\text{TransBoost}\)  is inspired by TSVMs [1]}, [2]} and follows a large margin principle.
